Frontiers in immunology · 2026
CT-derived topological intratumoral heterogeneity predicts major pathological response to neoadjuvant immunochemotherapy in resectable non-small-cell lung cancer: a two-center study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Zhou Yongjie, Hong Tingyu, Qi Hongliang, Zhao Jinhong, Wu Zhengyang, Du Jingjing, Liu Lan, Zou Fei
Аннотация
OBJECTIVES: To develop and validate a pretreatment computed tomography (CT)-derived model for predicting major pathological response (MPR) to neoadjuvant immunochemotherapy in resectable non-small-cell lung cancer (NSCLC).
METHODS: This multicenter retrospective study included 287 patients with stage IB-III NSCLC treated with neoadjuvant immunotherapy plus chemotherapy followed by surgery. Patients from Center A formed the training cohort (n = 224), and patients from Center B formed the external validation cohort (n = 63). Two-dimensional (2D) and three-dimensional (3D) intratumoral heterogeneity scores (ITHscores) were derived from venous-phase pretreatment CT using local radiomics, K-means clustering and connected-component topology. Candidate predictors included clinicopathological variables, ITHscores and blood-derived inflammatory indicators. Eight machine-learning algorithms were compared, and the final model was selected using cross-validation and feature ablation. Shapley additive explanations (SHAP) analysis was used to interpret feature contributions.
RESULTS: Non-MPR tumors showed significantly higher 2D and 3D ITHscores than MPR tumors in both cohorts (all P < 0.001). CatBoost was selected as the optimal algorithm. SHAP analysis identified 2D ITHscore and 3D ITHscore as the two most influential predictors, with mean absolute SHAP values of 0.403 and 0.326, respectively. The final parsimonious model retained 2D ITHscore, 3D ITHscore and systemic immune-inflammation index. It achieved AUCs of 0.824 in the training cohort and 0.792 in the external validation cohort, outperforming the clinical and ITH-only models. Calibration and decision curve analyses showed acceptable agreement and potential clinical utility.
CONCLUSION: CT-derived topological intratumoral heterogeneity, combined with systemic inflammation, may provide a non-invasive pretreatment biomarker for predicting MPR in resectable NSCLC.
METHODS: This multicenter retrospective study included 287 patients with stage IB-III NSCLC treated with neoadjuvant immunotherapy plus chemotherapy followed by surgery. Patients from Center A formed the training cohort (n = 224), and patients from Center B formed the external validation cohort (n = 63). Two-dimensional (2D) and three-dimensional (3D) intratumoral heterogeneity scores (ITHscores) were derived from venous-phase pretreatment CT using local radiomics, K-means clustering and connected-component topology. Candidate predictors included clinicopathological variables, ITHscores and blood-derived inflammatory indicators. Eight machine-learning algorithms were compared, and the final model was selected using cross-validation and feature ablation. Shapley additive explanations (SHAP) analysis was used to interpret feature contributions.
RESULTS: Non-MPR tumors showed significantly higher 2D and 3D ITHscores than MPR tumors in both cohorts (all P < 0.001). CatBoost was selected as the optimal algorithm. SHAP analysis identified 2D ITHscore and 3D ITHscore as the two most influential predictors, with mean absolute SHAP values of 0.403 and 0.326, respectively. The final parsimonious model retained 2D ITHscore, 3D ITHscore and systemic immune-inflammation index. It achieved AUCs of 0.824 in the training cohort and 0.792 in the external validation cohort, outperforming the clinical and ITH-only models. Calibration and decision curve analyses showed acceptable agreement and potential clinical utility.
CONCLUSION: CT-derived topological intratumoral heterogeneity, combined with systemic inflammation, may provide a non-invasive pretreatment biomarker for predicting MPR in resectable NSCLC.